{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data2的数据是\n",
      "1039\n",
      "数据量是\n",
      "data2的数据是\n",
      "1039\n",
      "数据量是\n",
      "D:\\Projects\\PycharmProjects\\DataA\\A102\\task2_1.xlsx\n",
      "data2的数据是\n",
      "1039\n",
      "数据量是\n",
      "D:\\Projects\\PycharmProjects\\DataA\\A103\\task2_1.xlsx\n",
      "data2的数据是\n",
      "1039\n",
      "数据量是\n",
      "data2的数据是\n",
      "1039\n",
      "数据量是\n",
      "D:\\Projects\\PycharmProjects\\DataA\\A105\\task2_1.xlsx\n",
      "data2的数据是\n",
      "1039\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Input \u001B[1;32mIn [118]\u001B[0m, in \u001B[0;36m<cell line: 9>\u001B[1;34m()\u001B[0m\n\u001B[0;32m     20\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m data1\u001B[38;5;241m.\u001B[39mvalues:\n\u001B[0;32m     21\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m i[\u001B[38;5;241m9\u001B[39m] \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mlist\u001B[39m(d2):\n\u001B[1;32m---> 22\u001B[0m         tmp\u001B[38;5;241m.\u001B[39mloc[j] \u001B[38;5;241m=\u001B[39m i\n\u001B[0;32m     23\u001B[0m         j \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m     24\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m数据量是\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\indexing.py:818\u001B[0m, in \u001B[0;36m_LocationIndexer.__setitem__\u001B[1;34m(self, key, value)\u001B[0m\n\u001B[0;32m    815\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_has_valid_setitem_indexer(key)\n\u001B[0;32m    817\u001B[0m iloc \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miloc\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39miloc\n\u001B[1;32m--> 818\u001B[0m \u001B[43miloc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_setitem_with_indexer\u001B[49m\u001B[43m(\u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mname\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\indexing.py:1785\u001B[0m, in \u001B[0;36m_iLocIndexer._setitem_with_indexer\u001B[1;34m(self, indexer, value, name)\u001B[0m\n\u001B[0;32m   1782\u001B[0m     indexer, missing \u001B[38;5;241m=\u001B[39m convert_missing_indexer(indexer)\n\u001B[0;32m   1784\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m missing:\n\u001B[1;32m-> 1785\u001B[0m         \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_setitem_with_indexer_missing\u001B[49m\u001B[43m(\u001B[49m\u001B[43mindexer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1786\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[0;32m   1788\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mloc\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m   1789\u001B[0m     \u001B[38;5;66;03m# must come after setting of missing\u001B[39;00m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\indexing.py:2182\u001B[0m, in \u001B[0;36m_iLocIndexer._setitem_with_indexer_missing\u001B[1;34m(self, indexer, value)\u001B[0m\n\u001B[0;32m   2180\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_mgr \u001B[38;5;241m=\u001B[39m df\u001B[38;5;241m.\u001B[39m_mgr\n\u001B[0;32m   2181\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 2182\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_mgr \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_append\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvalue\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39m_mgr\n\u001B[0;32m   2183\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mobj\u001B[38;5;241m.\u001B[39m_maybe_update_cacher(clear\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\frame.py:9795\u001B[0m, in \u001B[0;36mDataFrame._append\u001B[1;34m(self, other, ignore_index, verify_integrity, sort)\u001B[0m\n\u001B[0;32m   9792\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   9793\u001B[0m     to_concat \u001B[38;5;241m=\u001B[39m [\u001B[38;5;28mself\u001B[39m, other]\n\u001B[1;32m-> 9795\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mconcat\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   9796\u001B[0m \u001B[43m    \u001B[49m\u001B[43mto_concat\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   9797\u001B[0m \u001B[43m    \u001B[49m\u001B[43mignore_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mignore_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   9798\u001B[0m \u001B[43m    \u001B[49m\u001B[43mverify_integrity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverify_integrity\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   9799\u001B[0m \u001B[43m    \u001B[49m\u001B[43msort\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   9800\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   9801\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\u001B[38;5;241m.\u001B[39m__finalize__(\u001B[38;5;28mself\u001B[39m, method\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mappend\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\util\\_decorators.py:331\u001B[0m, in \u001B[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    325\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m num_allow_args:\n\u001B[0;32m    326\u001B[0m     warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m    327\u001B[0m         msg\u001B[38;5;241m.\u001B[39mformat(arguments\u001B[38;5;241m=\u001B[39m_format_argument_list(allow_args)),\n\u001B[0;32m    328\u001B[0m         \u001B[38;5;167;01mFutureWarning\u001B[39;00m,\n\u001B[0;32m    329\u001B[0m         stacklevel\u001B[38;5;241m=\u001B[39mfind_stack_level(),\n\u001B[0;32m    330\u001B[0m     )\n\u001B[1;32m--> 331\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:381\u001B[0m, in \u001B[0;36mconcat\u001B[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001B[0m\n\u001B[0;32m    159\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    160\u001B[0m \u001B[38;5;124;03mConcatenate pandas objects along a particular axis.\u001B[39;00m\n\u001B[0;32m    161\u001B[0m \n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    366\u001B[0m \u001B[38;5;124;03m1   3   4\u001B[39;00m\n\u001B[0;32m    367\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    368\u001B[0m op \u001B[38;5;241m=\u001B[39m _Concatenator(\n\u001B[0;32m    369\u001B[0m     objs,\n\u001B[0;32m    370\u001B[0m     axis\u001B[38;5;241m=\u001B[39maxis,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    378\u001B[0m     sort\u001B[38;5;241m=\u001B[39msort,\n\u001B[0;32m    379\u001B[0m )\n\u001B[1;32m--> 381\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mop\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_result\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:616\u001B[0m, in \u001B[0;36m_Concatenator.get_result\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    612\u001B[0m             indexers[ax] \u001B[38;5;241m=\u001B[39m obj_labels\u001B[38;5;241m.\u001B[39mget_indexer(new_labels)\n\u001B[0;32m    614\u001B[0m     mgrs_indexers\u001B[38;5;241m.\u001B[39mappend((obj\u001B[38;5;241m.\u001B[39m_mgr, indexers))\n\u001B[1;32m--> 616\u001B[0m new_data \u001B[38;5;241m=\u001B[39m \u001B[43mconcatenate_managers\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    617\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmgrs_indexers\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mnew_axes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconcat_axis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbm_axis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcopy\u001B[49m\n\u001B[0;32m    618\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    619\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcopy:\n\u001B[0;32m    620\u001B[0m     new_data\u001B[38;5;241m.\u001B[39m_consolidate_inplace()\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\internals\\concat.py:233\u001B[0m, in \u001B[0;36mconcatenate_managers\u001B[1;34m(mgrs_indexers, axes, concat_axis, copy)\u001B[0m\n\u001B[0;32m    231\u001B[0m     fastpath \u001B[38;5;241m=\u001B[39m blk\u001B[38;5;241m.\u001B[39mvalues\u001B[38;5;241m.\u001B[39mdtype \u001B[38;5;241m==\u001B[39m values\u001B[38;5;241m.\u001B[39mdtype\n\u001B[0;32m    232\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 233\u001B[0m     values \u001B[38;5;241m=\u001B[39m \u001B[43m_concatenate_join_units\u001B[49m\u001B[43m(\u001B[49m\u001B[43mjoin_units\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconcat_axis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    234\u001B[0m     fastpath \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m    236\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m fastpath:\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\internals\\concat.py:577\u001B[0m, in \u001B[0;36m_concatenate_join_units\u001B[1;34m(join_units, concat_axis, copy)\u001B[0m\n\u001B[0;32m    574\u001B[0m     concat_values \u001B[38;5;241m=\u001B[39m ensure_block_shape(concat_values, \u001B[38;5;241m2\u001B[39m)\n\u001B[0;32m    576\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 577\u001B[0m     concat_values \u001B[38;5;241m=\u001B[39m \u001B[43mconcat_compat\u001B[49m\u001B[43m(\u001B[49m\u001B[43mto_concat\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mconcat_axis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    579\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m concat_values\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\pandasLearning\\lib\\site-packages\\pandas\\core\\dtypes\\concat.py:151\u001B[0m, in \u001B[0;36mconcat_compat\u001B[1;34m(to_concat, axis, ea_compat_axis)\u001B[0m\n\u001B[0;32m    148\u001B[0m             to_concat \u001B[38;5;241m=\u001B[39m [x\u001B[38;5;241m.\u001B[39mastype(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mobject\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m to_concat]\n\u001B[0;32m    149\u001B[0m             kinds \u001B[38;5;241m=\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mo\u001B[39m\u001B[38;5;124m\"\u001B[39m}\n\u001B[1;32m--> 151\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconcatenate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mto_concat\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maxis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maxis\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    152\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m kinds \u001B[38;5;129;01mand\u001B[39;00m result\u001B[38;5;241m.\u001B[39mdtype\u001B[38;5;241m.\u001B[39mkind \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mi\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mu\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m]:\n\u001B[0;32m    153\u001B[0m     \u001B[38;5;66;03m# GH#39817\u001B[39;00m\n\u001B[0;32m    154\u001B[0m     warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m    155\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mBehavior when concatenating bool-dtype and numeric-dtype arrays is \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    156\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdeprecated; in a future version these will cast to object dtype \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    160\u001B[0m         stacklevel\u001B[38;5;241m=\u001B[39mfind_stack_level(),\n\u001B[0;32m    161\u001B[0m     )\n",
      "File \u001B[1;32m<__array_function__ internals>:180\u001B[0m, in \u001B[0;36mconcatenate\u001B[1;34m(*args, **kwargs)\u001B[0m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "s = 0\n",
    "#参赛作品\n",
    "data2 = pd.read_excel('D:\\Projects\\PycharmProjects\\data\\A题：竞赛作品的自动评判数据\\criteria2_1.xlsx')\n",
    "d2 = data2['正式登记证号']\n",
    "sss=''\n",
    "str = 'D:\\Projects\\PycharmProjects\\DataA\\A{ss}\\\\task2_1.xlsx'\n",
    "for i in range(101, 999):\n",
    "    sss=str.format(ss=i)\n",
    "    data1 = pd.read_excel(str.format(ss=i))\n",
    "    #这个是标准\n",
    "    # print(len(data2))\n",
    "    print('data2的数据是')\n",
    "    print(len(d2))\n",
    "    tmp = data2\n",
    "    tmp.drop(tmp.index, inplace=True)\n",
    "    #获取一个空的表\n",
    "    j = 0\n",
    "    for i in data1.values:\n",
    "        if i[9] in list(d2):\n",
    "            tmp.loc[j] = i\n",
    "            j += 1\n",
    "    print('数据量是')\n",
    "    if len(tmp)==0:\n",
    "        print(sss)\n",
    "    # print(len(tmp))\n",
    "\n",
    "    # for i in tmp.values:\n",
    "    #     # data2['正式登记证号']\n",
    "    #     # 9\n",
    "    #     # print(i[2])\n",
    "    #     for j in data2.values:\n",
    "    #         if i[9] == j[9]:\n",
    "    #             # print(True)\n",
    "    #             # print(i[9],j[9])\n",
    "    #             if i[2] != j[2]:\n",
    "    #                 print(i[2], j[2])\n",
    "    #                 s += 1\n",
    "    # print(s)\n",
    "\n",
    "\n",
    "\n",
    "# print(i)\n",
    "# d1 = data1['产品通用名称']\n",
    "# print(len(data2['正式登记证号'].unique()))\n",
    "# print(len(data2['正式登记证号']))\n",
    "#获取一个新的数据\n",
    "# data1.loc[data1['正式登记证号'] in data2['正式登记证号'],:]\n",
    "# #rename 重新设置我们的列名 然后设置inplace=True\n",
    "# data1.rename(columns={'产品通用名称': '产品通用名称作品'}, inplace=True)\n",
    "# # data1.head(2)\n",
    "# table = pd.merge(data1, data2, on=['正式登记证号', '正式登记证号'])\n",
    "#保存到excel文件里面\n",
    "# table.to_excel('mearge.xlsx')\n",
    "# # table.head(2)\n",
    "# for i in table.values:\n",
    "#     print(i[2] + i[15])\n",
    "#     if i[2]==i[15]:\n",
    "#         s+=1\n",
    "# print(s)\n",
    "# merges"
   ]
  },
  {
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   "execution_count": null,
   "outputs": [],
   "source": [],
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